Abstract
Giving feedback to students is not just about marking their answers as correct or incorrect, but also finding mistakes in their thought process that led them to that incorrect answer. In this paper, we introduce a machine learning technique for mistake captioning, a task that attempts to identify mistakes and provide feedback meant to help learners correct these mistakes. We do this by training a sequence-to-sequence network to generate this feedback based on domain experts. To evaluate this system, we explore how it can be used on a Linguistics assignment studying Grimm's Law. We show that our approach generates feedback that outperforms a baseline on a set of automated NLP metrics. In addition, we perform a series of case studies in which we examine successful and unsuccessful system outputs.
Original language | English |
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Title of host publication | International Conference Recent Advances in Natural Language Processing, RANLP 2021 |
Subtitle of host publication | Deep Learning for Natural Language Processing Methods and Applications - Proceedings |
Editors | Galia Angelova, Maria Kunilovskaya, Ruslan Mitkov, Ivelina Nikolova-Koleva |
Pages | 1455-1462 |
Number of pages | 8 |
ISBN (Electronic) | 9789544520724 |
DOIs | |
State | Published - 2021 |
Event | International Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021 - Virtual, Online Duration: Sep 1 2021 → Sep 3 2021 |
Publication series
Name | International Conference Recent Advances in Natural Language Processing, RANLP |
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ISSN (Print) | 1313-8502 |
Conference
Conference | International Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021 |
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City | Virtual, Online |
Period | 9/1/21 → 9/3/21 |
Bibliographical note
Publisher Copyright:© 2021 Incoma Ltd. All rights reserved.
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Artificial Intelligence
- Electrical and Electronic Engineering